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A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications

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Abstract

The elimination of noisy content from digital images is one of the major issues during image pre-processing. The process of image acquisition, compression, and image transmission is a major reason for image noise that causes loss of information. This loss of information causes irregularities and error in the working of many real-time applications such as computerized photography, hurdle detection and traffic monitoring (computer vision), automatic character recognition, morphing, and surveillance applications. This paper proposes a new hybrid and multi-level digital image denoising approach (MLAC) using a convolutional neural network (CNN) and anisotropic diffusion (AD). The denoising approach uses a hybrid combination of CNN and AD using multi-level implementation. First of all, CNN is applied to noisy images for noise elimination, which results in a denoised image in the first level of image denoising. After that, denoised image is passed to AD in the second level of image denoising. The AD is applied for edge and corner preservation of objects. This hybrid approach is highly efficient in removing noise while preserving fine details of image. The proposed denoising method is experimented on all standard inbuilt image datasets of Matlab framework. It is tested on SAR images as well. The results are compared with those of some of the latest works in the field of CNN and AD. The quality of the denoised image is tested by using naked eye visual analysis factors and quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), universal image quality index (UIQI), feature similarity index metric (FSIM), equivalent numbers of looks (ENL), noise variance (NV), and mean-squared error (MSE). The denoising results are further critically analyzed using zooming analysis method, plotting histogram, comparative running real-time implementation aspects, and time complexity evaluation. The detailed study of result confirms that the proposed approach gives an excellent result in terms of structure, edge preservation, and noise suppression.

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References

  1. Zhang, Cai, Fei Du, and Yungang Zhang. "A Brief Review of Image Restoration Techniques Based on Generative Adversarial Models." Advanced Multimedia and Ubiquitous Engineering. Springer, Singapore, 2019. 169-175

  2. Guo, Q., et al.: An efficient SVD-based method for image denoising. IEEE Trans. Circuits Syst. Video Technol. 26(5), 868–880 (2015)

    Article  Google Scholar 

  3. Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)

    Article  Google Scholar 

  4. Romano, Y., Elad, M.: Boosting of image denoising algorithms. SIAM J. Imaging Sci. 8(2), 1187–1219 (2015)

    Article  MathSciNet  Google Scholar 

  5. Ghose, S., Singh, N., Singh, P.: Image denoising using deep learning: convolutional neural network. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 511–517. IEEE (2020)

  6. Liu, Z., Yan, W.Q., Yang, M.L.: Image denoising based on a CNN model. In 2018 4th International Conference on Control, Automation and Robotics (ICCAR), pp. 389–393. IEEE (2018)

  7. Zhang, Y., et al.: A patch based denoising method using deep convolutional neural network for seismic image. IEEE Access 7, 156883–156894 (2019)

    Article  Google Scholar 

  8. Bolsee, Q., Munteanu, A.: Cnn-based denoising of Time-Of-Flight depth images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 510–514. IEEE (2018)

  9. Lin, B., Tao, X., Qin, X., Duan, Y., Lu, J.: Hyperspectral image denoising via nonnegative matrix factorization and convolutional neural networks. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 4023–4026. IEEE (2018)

  10. Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.M., Cremers, D.: Anisotropic diffusion descriptors. Comput Graph Forum 35,(2) 431–441 (2016)

    Article  Google Scholar 

  11. Bavirisetti, D.P., Dhuli, R.: Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sens. J. 16(1), 203–209 (2015)

    Article  Google Scholar 

  12. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  13. Muthukumaran, M., Prabaharan, L., Sivapathi, A., Gopalakrishnan, S.: A comparative analysis of an anisotropic diffusion image denoising methods on weld X-radiography images. Far East J. Electron. Commun. 17(2), 267–281 (2017)

    Article  Google Scholar 

  14. Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M.K., Zhang, Y., Sun, L., Wang, G.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)

    Article  Google Scholar 

  15. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  16. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)

    Article  MathSciNet  Google Scholar 

  17. Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3204–3213 (2018)

  18. Bai, J., Feng, X.-C.: Image denoising using generalized anisotropic diffusion. J. Math. Imaging Vis. 60, 994–1007 (2018). https://doi.org/10.1007/s10851-018-0790-4

    Article  MathSciNet  MATH  Google Scholar 

  19. Srinivas, A., Amit, J.: De noising techniques for ultrasound images using anisotropic diffusion filtration method and its impact. Int. J. IT Eng. 6(3), 8–13 (2018)

    Google Scholar 

  20. Gong, K., Guan, J., Liu, C.-C., Qi, J.: PET image denoising using a deep neural network through fine tuning. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 153–161 (2018)

    Article  Google Scholar 

  21. Xin, H., Feng, L.: Research on image denoising algorithm based on improved anisotropic diffusion synthetic aperture radar. In: Tenth International Conference on Graphics and Image Processing (ICGIP 2018), International Society for Optics and Photonics, Vol. 11069, p. 110692I (2019)

  22. Saravani, S., Shad, R., Ghaemi, M.: Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain. Multimed. Tools Appl. 77, 31469–31486 (2018). https://doi.org/10.1007/s11042-018-6153-8

    Article  Google Scholar 

  23. Tian, C., Xu, Y., Fei, L., Wang, J., Wen, J., Luo, N.: Enhanced CNN for image denoising. CAAI Trans. Intell. Technol. 4(1), 17–23 (2019)

    Article  Google Scholar 

  24. Tian, C., Xu, Y., Zuo, W.: Image denoising using deep CNN with batch renormalization. Neural Netw. 121(2020), 461–473 (2020)

    Article  Google Scholar 

  25. Singh, P., Shree, R.: A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J King Saud. Univ. Comput. Inf. Sci. 32(1), 137–148 (2020)

    Google Scholar 

  26. Maffei, A., Haut, J.M., Paoletti, M.E., Plaza, J., Bruzzone, L., Plaza, A.: A Single Model CNN for Hyperspectral Image Denoising. IEEE Trans. Geosci. Remote Sens. 58(4), 2516–2529 (2020)

    Article  Google Scholar 

  27. Goyal, B., Dogra, A., Agrawal, S., Sohi, B.S., Sharma, A.: Image denoising review: from classical to state-of-the-art approaches. Inf. Fus. 55, 220–244 (2020)

    Article  Google Scholar 

  28. Aggarwal, A., Rani, A., Kumar, M.: A robust method to authenticate license plates using segmentation and ROI based approach. Smart Sustain. Built Environ. (2019). https://doi.org/10.1108/SASBE-07-2019-0083

    Article  Google Scholar 

  29. Kumar, M., Srivastava, S.: Image authentication by assessing manipulations using illumination. Multimed. Tools Appl. 78(9), 12451–21246 (2019)

    Article  Google Scholar 

  30. Fan, L., Zhang, F., Fan, H., Zhang, C.: Brief review of image denoising techniques. Vis. Comput. Ind. Biomed. Art. (2019). https://doi.org/10.1186/s42492-019-0016-7

    Article  Google Scholar 

  31. Kumar, M., Srivastava, S., Uddin, N.: Forgery detection using multiple light sources for synthetic images. Aus. J. Forensic Sci. 51(3), 243–250 (2017)

    Article  Google Scholar 

  32. Singh, P., Shree, R.: Quantitative dual nature analysis of mean square error in SAR image despeckling. Int. J. Comput. Sci. Eng. (IJCSE) 9(11), 619–622 (2017)

    Google Scholar 

  33. Singh, P., Shree, R.: A new computationally improved homomorphic despeckling technique of SAR images. Int. J. Adv. Res. Comput. Sci. 8(3) (2017). https://doi.org/10.26483/ijarcs.v8i3.3122

    Article  Google Scholar 

  34. Diwakar, M., Singh, P.: CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain. Biomed. Signal Process. Control 57, 101754 (2020)

    Article  Google Scholar 

  35. Singh, P., Shree, R.: Impact of Method Noise on SAR Image Despeckling. Int. J. Inf. Technol. Web Eng. (IJITWE) 15(1), 52–63 (2020)

    Article  Google Scholar 

  36. Singh, P., Shree, R.: Importance of DWT in despeckling SAR images and experimentally analyzing the wavelet based thresholding techniques. Int. J. Eng. Sci Res. Technol. 5(10) (2016). https://doi.org/10.5281/zenodo.160861

    Article  Google Scholar 

  37. Zhang, L., et al.: FSIM: A feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

  38. Singh, P., Shree, R.: A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J. King Saud Univ. Comput. Inf. Sci. 32(1), 137–148 (2020)

    Google Scholar 

  39. He, K., & Sun, J.: Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5353–5360 (2015)

  40. Tian, C., et al.: Attention-guided CNN for image denoising. Neural Netw. 124, 117–129 (2020)

    Article  Google Scholar 

  41. Yu, S., Park, B., & Jeong, J.: Deep iterative down-up CNN for image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

  42. BinMakhashen, G.M., Mahmoud, S.A.: Historical document layout analysis using anisotropic diffusion and geometric features. Int J Digit Libr. (2020). https://doi.org/10.1007/s00799-020-00280-w

    Article  Google Scholar 

  43. Singh, P., Shree, R.: A new SAR image despeckling using directional smoothing filter and method noise thresholding. Eng. Sci. Technol. Int. J. 21(4), 589–610 (2018)

    Google Scholar 

  44. DATASET OF STANDARD 512X512 GRAYSCALE TEST IMAGES, Available at: http://decsai.ugr.es/cvg/CG/base.htm. Accessed 20 July 2020

  45. Synthetic Aperture Radar (SAR) Imagery Dataset, Available at: https://www.sandia.gov/RADAR/imagery/. Accessed 20 July 2020

  46. Jiang, Y., Yuan, R., Sun, Y., Tian, J.: Image denoising based on noise detection. In: IOP Conference Series: Materials Science and Engineering, Vol. 322, No. 7, p. 072050 (2018)

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  1. Department of CSE, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

    Prabhishek Singh & Achyut Shankar

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  1. Prabhishek Singh
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Singh, P., Shankar, A. A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications. J Real-Time Image Proc 18, 1711–1728 (2021). https://doi.org/10.1007/s11554-020-01060-0

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